n mix results

N - mixture models by species

modelling N by site to get relative abundance

abundance by site will be used as a cov on predator occupancy

7 models evaluated, dot, jdt + jdtSQ, lure + jdt + jdtSQ

Species:      GoldenMantledGroundSquirrel

Metadata Summary:

N_sites	N_counts	N_detections	rep_period	iterations	burnin	thin
127	626	140	7 days	120000	20000	10

Detections by Year:

Yr	2016	2017	2018	2019	2020
sites	19	31	19	32	26
detections	22	55	16	18	29
N.dot.model	10	13	8	7	16

WAIC

Models by WAIC:

model	description	WAIC	N.total.est
fm7	counts	21.94854	127
fm6	lure + jdt + jdtSq	692.44325	58
fm5	jdt + jdtSq	710.85324	50
fm4	lure + jdt	731.56568	60
fm2	jdt	740.01517	51
fm1	dot	821.73284	54
fm3	lure	830.35690	57

Model summaries:

model: fm1

dot

summary table

param	covariate	iters_ef	mean	mode	hdi_89pct_lower	hdi_89pct_upper	bayes_P	pct.density
p[1]	NA	4411	0.099	0.092	0.05	0.15	0	1.001
p[2]	NA	8029	0.187	0.18	0.14	0.23	0	1.001
p[3]	NA	4186	0.047	0.044	0.02	0.08	0	1.001
p[4]	NA	7430	0.072	0.07	0.04	0.10	0	1.001
p[5]	NA	4587	0.052	0.049	0.03	0.08	0	1.001
lambda[1]	NA	3263	0.633	0.517	0.23	0.98	0	1.001
lambda[2]	NA	9003	0.418	0.361	0.21	0.62	0	1.001
lambda[3]	NA	3013	0.618	0.445	0.19	1.02	0	1.001
lambda[4]	NA	8162	0.301	0.241	0.10	0.48	0	1.001
lambda[5]	NA	4666	0.836	0.682	0.41	1.24	0	1.001
N[110]	NA	7837	0.157	err	0.00	1.00	err	err
N[78]	NA	10000	0.119	err	0.00	1.00	err	err
N[54]	NA	5237	0.165	err	0.00	1.00	err	err
N[40]	NA	0	0.000	err	0.00	0.00	err	err
N[83]	NA	9488	1.072	err	1.00	1.00	err	err

p[1]

p[2]

p[3]

p[4]

p[5]

lambda[1]

lambda[2]

lambda[3]

lambda[4]

lambda[5]

N[110]

N[78]

N[54]

N[40]

## Warning in cor(X, use = "pairwise.complete.obs"): the standard deviation is zero

## Warning: Removed 50 rows containing missing values (geom_bar).

N[83]

model: fm2

jdt

summary table

param	covariate	iters_ef	mean	mode	hdi_89pct_lower	hdi_89pct_upper	bayes_P	pct.density
alpha	jdt	5050	1.196	1.212	0.98	1.41	0	1.001
alpha0	NA	4191	-2.942	-2.935	-3.22	-2.67	0	1.001
lambda[1]	NA	9140	0.576	0.518	0.27	0.87	0	1.001
lambda[2]	NA	9246	0.653	0.598	0.33	0.97	0	1.001
lambda[3]	NA	9712	0.467	0.409	0.19	0.72	0	1.001
lambda[4]	NA	10000	0.258	0.22	0.10	0.41	0	1.001
lambda[5]	NA	9521	0.724	0.67	0.40	1.02	0	1.001
N[85]	NA	10000	1.028	err	1.00	1.00	err	err
N[68]	NA	9627	1.086	err	1.00	1.00	err	err
N[32]	NA	9295	1.422	1	1.00	2.00	0	1.0001
N[114]	NA	10000	0.072	err	0.00	0.00	err	err
N[40]	NA	10000	0.016	err	0.00	0.00	err	err

alpha

alpha0

lambda[1]

lambda[2]

lambda[3]

lambda[4]

lambda[5]

N[85]

N[68]

N[32]

N[114]

N[40]

alpha relationship

model: fm3

lure

summary table

param	covariate	iters_ef	mean	mode	hdi_89pct_lower	hdi_89pct_upper	bayes_P	pct.density
alpha	lure	8065	-0.093	-0.074	-0.26	0.07	0.68622	0.8133
alpha0	NA	5904	-2.449	-2.441	-2.65	-2.23	0	1.001
lambda[1]	NA	9196	0.648	0.554	0.30	0.99	0	1.001
lambda[2]	NA	7422	0.749	0.67	0.40	1.07	0	1.001
lambda[3]	NA	10000	0.411	0.326	0.16	0.63	0	1.001
lambda[4]	NA	9650	0.271	0.234	0.11	0.42	0	1.001
lambda[5]	NA	9422	0.652	0.603	0.37	0.94	0	1.001
N[56]	NA	10000	0.020	err	0.00	0.00	err	err
N[122]	NA	10000	1.141	err	1.00	2.00	err	err
N[54]	NA	10000	0.020	err	0.00	0.00	err	err
N[104]	NA	9412	1.141	err	1.00	2.00	err	err
N[14]	NA	10000	0.090	err	0.00	0.00	err	err

alpha

alpha0

lambda[1]

lambda[2]

lambda[3]

lambda[4]

lambda[5]

N[56]

N[122]

N[54]

N[104]

N[14]

alpha relationship

model: fm4

lure + jdt

summary table

param	covariate	iters_ef	mean	mode	hdi_89pct_lower	hdi_89pct_upper	bayes_P	pct.density
alpha[1]	lureDays	3620	-0.602	-0.601	-0.83	-0.38	0	1.001
alpha[2]	julianDt	3255	1.477	1.477	1.21	1.74	0	1.001
alpha0	NA	2557	-3.152	-3.129	-3.47	-2.83	0	1.001
lambda[1]	NA	10000	0.475	0.421	0.21	0.72	0	1.001
lambda[2]	NA	8912	0.523	0.457	0.25	0.78	0	1.001
lambda[3]	NA	10000	0.444	0.375	0.16	0.67	0	1.001
lambda[4]	NA	9170	0.272	0.24	0.10	0.44	0	1.001
lambda[5]	NA	3270	1.353	1.173	0.62	2.02	0	1.001
N[88]	NA	8054	0.002	err	0.00	0.00	err	err
N[44]	NA	10681	0.460	0	0.00	1.00	1	err
N[113]	NA	6728	1.547	1	1.00	3.00	0	1.0001
N[77]	NA	10000	0.103	err	0.00	0.00	err	err
N[52]	NA	10000	0.032	err	0.00	0.00	err	err

alpha[1]

alpha[2]

alpha0

lambda[1]

lambda[2]

lambda[3]

lambda[4]

lambda[5]

N[88]

N[44]

N[113]

N[77]

N[52]

model: fm5

jdt + jdtSq

summary table

param	covariate	iters_ef	mean	mode	hdi_89pct_lower	hdi_89pct_upper	bayes_P	pct.density
alpha[1]	julianDt	1954	-0.053	-0.054	-0.39	0.29	0.96348	0.6068
alpha[2]	julianDtSq	1985	1.174	1.176	0.86	1.45	0	1.001
alpha0	NA	5409	-2.824	-2.822	-3.08	-2.56	0	1.001
lambda[1]	NA	9106	0.661	0.572	0.31	1.01	0	1.001
lambda[2]	NA	9606	0.466	0.402	0.22	0.70	0	1.001
lambda[3]	NA	9465	0.499	0.418	0.20	0.78	0	1.001
lambda[4]	NA	9419	0.237	0.212	0.09	0.38	0	1.001
lambda[5]	NA	9358	0.734	0.653	0.41	1.05	0	1.001
N[39]	NA	9669	0.017	err	0.00	0.00	err	err
N[63]	NA	10000	1.103	err	1.00	1.00	err	err
N[93]	NA	10000	0.000	err	0.00	0.00	err	err
N[59]	NA	9608	1.272	err	1.00	2.00	err	err
N[77]	NA	10000	0.001	err	0.00	0.00	err	err

alpha[1]

alpha[2]

alpha0

lambda[1]

lambda[2]

lambda[3]

lambda[4]

lambda[5]

N[39]

N[63]

N[93]

N[59]

N[77]

julian date relationship

model: fm6

lure + jdt + jdtSq

summary table

param	covariate	iters_ef	mean	mode	hdi_89pct_lower	hdi_89pct_upper	bayes_P	pct.density
alpha[1]	lureDays	3711	-0.636	-0.619	-0.83	-0.43	0	1.001
alpha[2]	julianDt	2108	0.231	0.227	-0.13	0.57	0.6141	0.8493
alpha[3]	julianDtSq	2326	1.216	1.23	0.92	1.53	0	1.001
alpha0	NA	3246	-3.123	-3.12	-3.44	-2.80	0	1.001
lambda[1]	NA	9102	0.524	0.435	0.23	0.79	0	1.001
lambda[2]	NA	10000	0.428	0.414	0.20	0.64	0	1.001
lambda[3]	NA	8902	0.479	0.406	0.20	0.75	0	1.001
lambda[4]	NA	9823	0.248	0.202	0.10	0.39	0	1.001
lambda[5]	NA	3421	1.411	1.199	0.67	2.10	0	1.001
N[89]	NA	10303	0.178	err	0.00	1.00	err	err
N[100]	NA	10000	0.000	err	0.00	0.00	err	err
N[24]	NA	10000	0.001	err	0.00	0.00	err	err
N[93]	NA	10000	0.000	err	0.00	0.00	err	err
N[38]	NA	10000	0.303	0	0.00	1.00	1	err

alpha[1]

alpha[2]

alpha[3]

alpha0

lambda[1]

lambda[2]

lambda[3]

lambda[4]

lambda[5]

N[89]

N[100]

N[24]

N[93]

N[38]

model: fm7

counts

summary table

param	covariate	iters_ef	mean	mode	hdi_89pct_lower	hdi_89pct_upper	bayes_P	pct.density
alpha	counts	8541	16.781	16.592	14.61	18.87	0	1.001
alpha0	NA	8206	-5.162	-5.122	-5.96	-4.38	0	1.001
lambda[1]	NA	9903	0.982	0.877	0.43	1.48	0	1.001
lambda[2]	NA	8693	0.949	0.851	0.45	1.40	0	1.001
lambda[3]	NA	9152	0.959	0.855	0.37	1.48	0	1.001
lambda[4]	NA	6580	0.915	0.725	0.34	1.49	0	1.001
lambda[5]	NA	10000	0.971	0.913	0.52	1.39	0	1.001
N[85]	NA	7711	1.001	err	1.00	1.00	err	err
N[62]	NA	10576	1.047	err	1.00	1.00	err	err
N[49]	NA	9657	0.913	0	0.00	2.00	1	err
N[91]	NA	9385	0.865	0	0.00	2.00	1	err
N[27]	NA	0	1.000	err	1.00	1.00	err	err

alpha

alpha0

lambda[1]

lambda[2]

lambda[3]

lambda[4]

lambda[5]

N[85]

N[62]

N[49]

N[91]

N[27]

## Warning in cor(X, use = "pairwise.complete.obs"): the standard deviation is zero

## Warning: Removed 50 rows containing missing values (geom_bar).

alpha relationship